Resumen de Elección de Operadores Lógicos para la Inducción de Conocimiento Comprensible
Cristóbal Romero Morales, Sebastián Ventura Soto, César Hervás Martínez, Pedro González Espejo
In data mining, the quality of induced knowledge is determined by several features. The focus has been usually placed on accuracy, paying much less attention to comprehensibility. In this paper, we present a rule-based data mining system for classification. Our main goal is the analysis of the trade-off between accuracy and comprehensibility, but we approach comprehensibility from a novel point of view: we are interested in gaining insight into how the use of logical operators affects comprehensibility. In addition, we study the suitability of grammar-based genetic programming for data mining.
